ArticleFrontiers in applied mathematics and statistics2018
Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning.
Article in Frontiers in applied mathematics and statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PTSD subtypes and their underlying neural biomarkers: a systematic review.Psychological medicine · 2025Pooled it
- Disentangling global atrophy burden from local structural patterns reveals clinically relevant heterogeneity in mild cognitive impairment.Research square · 2026Article
- Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability.IEEE access : practical innovations, open solutions · 2025Article
- Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset.IEEE transactions on bio-medical engineering · 2021Article
- Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.Brain sciences · 2021Article
- Artificial intelligence as an emerging technology in the current care of neurological disorders.Journal of neurology · 2021Review
- A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning.Frontiers in genetics · 2021Article
- Neurofunctional Segmentation Shifts in the Hippocampus.Frontiers in human neuroscience · 2021Article
- Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.Personality neuroscience · 2021Article
- Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders.Heliyon · 2020Review
- Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.Data in brief · 2019Article
- Article
- Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance.Frontiers in neuroscience · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.